• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.112-124
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• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.9-16
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• 2004

In this paper, we propose a robust controller for robot manipulators with parametric uncertainties using feature-based visual servo control system. In order to improve trajectory error of the robot manipulators due to the parameter variation, integral action is included in the dynamic control of part in inner subroutine of the control system. This integral action also reduces feature error in the steady state. The stability analysis of the closed-loop system is shown by the Lyapunov method. The effectiveness of the proposed method is shown by simulation and experimental results on the 5 link robot manipulator with two degree of freedom.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1238-1245
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• 2014

This paper proposes an RBFN-based adaptive tracking controller for an electrically driven mobile robot with parametric uncertainties and external disturbances. A mobile robot model considered in this paper includes all models of the robot body and actuators with uncertain kinematic and dynamic parameters, and uncertain frictions and external disturbances. The proposed controller consists of an RBFN(Radial Basis Function Network) and a robust adaptive controller. The presented RBFN is used to approximate unknown nonlinear robot dynamic functions. The proposed controller is adjusted by the adaptation laws obtained through the Lyapunov stability analysis. The proposed control scheme does not a priori need the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. Also, nominal parameter values are not required in the controller. The global stability of the closed-loop robot control system is guaranteed using the Lyapunov stability theory. Simulation results show the validity and robustness of the proposed control scheme.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.2
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• pp.138-143
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• 1999

A new control method for the precision robust position control of a brushless DC(BLDC) motor for direct drive m motor(BLDDM) system using the asymptotically stable adaptive load torque observer is presented. A precision position c control is obtained for the BLDD motor system appro성mately linearized using the fieldlongrightarroworientation method. Many of t these motor systems have BLDD motor to obtain no backlashes. On the other hand, it has disadvantages such as the h high cost and more complex controller caused by the nonlinear characteristics. And the load torque disturbance is d directly affected to a motor shaft. To r밍ect this problem, stability analysis is calTied out using Lyapunov stability t theorem. Using this results, the stability is proved and load disturbance detected by the asymptotically stable adaptive observer is compensated by feedforwarding the equivalent CUlTent having the fast response.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.

• Journal of Mechanical Science and Technology
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• v.16 no.9
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• pp.1040-1053
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• 2002

This research deals with the influence of nonlinearities associated with impact and sliding upon the rocking behavior of a rigid block, which is subjected to two-dimensional horizontal and vertical excitation. Nonlinearities in the vibration were found to depend strongly on the effect of the impact between the block and the base, which involves an abrupt reduction in the system's kinetic energy. In particular, when sliding occurs, the rocking behavior is substantially changed. Response analysis using a non-dimensional rocking equation was carried out for a variety of excitation levels and excitation frequencies. The chaos responses were observed over a wide response region, particularly, in the cases of high vertical displacement and violent sliding motion, and the chaos characteristics appear in the time histories, Poincare maps, power spectra and Lyapunov exponents of the rocking responses. The complex behavior of chaotic response, in phase space, is illustrated by the Poincare map. The distribution of the rocking response is described by bifurcation diagrams and the effects of sliding motion are examined through the several rocking response examples.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.2
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• pp.159-165
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• 2015

Motion control schemes are generally classified into three categories (point stabilization, trajectory tracking, and path following). This paper deals with the problem which is associated with the initial deployment of a group of Unmanned Surface Vehicle (USVs) and corresponding point stabilization. To keep the formation of a group of USVs, it is necessary to set the relationship between each vehicle. A forcing functions such as potential fields are designed to keep the formation and a graph Laplacian is used to represent the connectivity between vehicle. In case of fixed topology of the graph representing the communication between the vehicles, the graph Laplacian is assumed constant. However the graph topologies are allowed to change as the vehicles move, and the system dynamics become discontinuous in nature because the graph Laplacian changes as time passes. To check the stability in the stage of deployment, the system is modeled with Kronecker algebra notation. Filippov's calculus of differential equations with discontinuous right hand sides is then used to formally characterize the behavior of USVs. The stability of the system is analyzed with Lyapunov's stability theory and LaSalle's invariance principle, and the validity is shown by checking the variation of state norm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2254-2259
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• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Journal of Mechanical Science and Technology
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• v.17 no.9
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• pp.1249-1260
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• 2003

This present work focuses on the influence of nonlinearities associated with impact on the rocking behavior of a rigid body block subjected to a two-dimensional excitation in the horizontal and vertical directions. The nonlinearities in rocking system are found to be strongly dependent on the impact between the block and the base that abruptly reduces the kinetic energy. In this study, the rocking systems of the two types are considered : The first is an undamped rocking system model that disregards the energy dissipation during the impact and the second is a damped rocking system, which incorporates energy dissipation during the impact. The response analysis is carried out by a numerical method using a non-dimensional rocking equation in which the variations in the excitation levels are considered. Chaos responses are observed over a wide range of parameter values, and particularly in the case of large vertical displacements, the chaotic characteristics are observed in the time histories, Poincare sections, the power spectral density and the largest Lyapunov exponents of the rocking responses. Complex behavior characteristics of rocking responses are illustrated by the Poincare sections.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.38-47
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• 2001

In this paper, a new scheme of adaptive-neuro control system is presented to implement real-time control of robot manipulator using Digital Signal Processors. Digital signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of therir prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust perfor-mance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method.The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for the implementation of real-time control of robot system by the simulation and experi-ment.